This project is aimed at developing a quantitative model of disease based on probabilistic foundations. Diseases are assumed to be characterized by the occurrence of at least one and possibly several implicants or disease components. Disease is diagnosed by using manifest information from patients, and from the causal relationship between implicants and indicants. Thus a patient may (and usually does at presenting time) exhibit a combination of indicants which could be the result of more than one disease (combination of implicants). The model, implemented by a computer program, produces the probability of all remotely credible combinations if there is sufficient available information to limit the number to available computer memory storage. The model needs parameters which define the frequency of different diseases in the studied group and the conditional probability of the occurrence of each indicant given a single implicant. The model assumes two important combinding rules. One (conditional independence assumption) permits the calculation of the probability of joint occurrence of indicants from their individual probabilities of occurrence. The other allows the computation of the probability of the occurrence of an indicant set, given the occurrence of several implicants. This formal model of diagnosis is being extended to provide a model of disease based on probabilities and using statistical methods to estimate the parameters.